Max Planck Institute for Empirical Aesthetics, 60322, Frankfurt, Germany.
Department of Psychology, Grinnell College, Grinnell, 50112 IA, USA.
Sci Rep. 2018 Mar 15;8(1):4570. doi: 10.1038/s41598-018-22933-2.
Music is thought to engage its listeners by driving feelings of surprise, tension, and relief through a dynamic mixture of predictable and unpredictable patterns, a property summarized here as "expressiveness". Birdsong shares with music the goal to attract its listeners' attention and might use similar strategies to achieve this. We here tested a thrush nightingale's (Luscinia luscinia) rhythm, as represented by song amplitude envelope (containing information on note timing, duration, and intensity), for evidence of expressiveness. We used multifractal analysis, which is designed to detect in a signal dynamic fluctuations between predictable and unpredictable states on multiple timescales (e.g. notes, subphrases, songs). Results show that rhythm is strongly multifractal, indicating fluctuations between predictable and unpredictable patterns. Moreover, comparing original songs with re-synthesized songs that lack all subtle deviations from the "standard" note envelopes, we find that deviations in note intensity and duration significantly contributed to multifractality. This suggests that birdsong is more dynamic due to subtle note timing patterns, often similar to musical operations like accelerando or crescendo. While different sources of these dynamics are conceivable, this study shows that multi-timescale rhythm fluctuations can be detected in birdsong, paving the path to studying mechanisms and function behind such patterns.
音乐通过动态混合可预测和不可预测的模式来引起听众的惊喜、紧张和放松感,这种特性可以概括为“表现力”。鸟鸣与音乐具有吸引听众注意力的共同目标,并且可能使用类似的策略来实现这一目标。在这里,我们测试了画眉莺(Luscinia luscinia)的节奏,以歌曲振幅包络(包含有关音符定时、持续时间和强度的信息)来表示,以寻找表现力的证据。我们使用了多重分形分析,该分析旨在检测信号中多个时间尺度(例如音符、子短语、歌曲)之间可预测和不可预测状态之间的动态波动。结果表明,节奏具有很强的多重分形性,表明存在可预测和不可预测的模式之间的波动。此外,将原始歌曲与缺乏所有“标准”音符包络细微偏差的重新合成歌曲进行比较,我们发现音符强度和持续时间的偏差对多重分形性有显著贡献。这表明鸟鸣由于微妙的音符定时模式而更加动态,通常类似于音乐操作,如渐快或渐强。虽然可以想象出这些动态的不同来源,但这项研究表明,可以在鸟鸣中检测到多时间尺度的节奏波动,为研究这些模式背后的机制和功能铺平了道路。